Abstract

WHAT IS KNOWN AND OBJECTIVES Paracetamol is a frequently used antipyretic and analgesic drug, but also a dose-dependent hepatotoxin. Unintentional paracetamol overdosing is a common medication error in hospitals. The present study aimed at (i) analysis of unintentional paracetamol overdosing in hospitalized patients; (ii) development, implementation and outcome analysis of an alert algorithm for the prevention of relevant paracetamol overdosing. METHODS All patients who received paracetamol in a Swiss tertiary care hospital during 2011 to 2014 were analysed to detect cases of paracetamol overdosing in a local pharmacoepidemiological database. In 2014, an automated algorithm screened the hospital's electronic prescribing system for patients at risk of overdosing, followed by expert validation. When imminent relevant overdosing was confirmed, alerts were issued to prescribers. Relevance was defined as prescriptions that permitted repeated daily paracetamol exposure of ≥5 g. RESULTS AND DISCUSSION From 2011 to 2013, relevant overdosing occurred in 11 patients (5-8 g/day for 3 to 5 days), which corresponds to 0·4 % of all patients exposed to any paracetamol overdosing (mean n = 988 per year). In 2014, alerts were issued by experts in 23 cases with subsequent changes to prescriptions in 21 (91·3 %) thereof. Although the occurrence of any paracetamol overdosing declined only marginally in 2014 (n = 914), no relevant overdosing occurred anymore. WHAT IS NEW AND CONCLUSION Unintentional paracetamol overdosing was frequent but only a small fraction thereof was deemed relevant. This proof of concept study analysed local hospital data and developed a preventive system combining sensitive automated detection with subsequent specific expert validation. The resulting alerts achieved high compliance and prevented relevant paracetamol overdosing.

Abstract

WHAT IS KNOWN AND OBJECTIVES Paracetamol is a frequently used antipyretic and analgesic drug, but also a dose-dependent hepatotoxin. Unintentional paracetamol overdosing is a common medication error in hospitals. The present study aimed at (i) analysis of unintentional paracetamol overdosing in hospitalized patients; (ii) development, implementation and outcome analysis of an alert algorithm for the prevention of relevant paracetamol overdosing. METHODS All patients who received paracetamol in a Swiss tertiary care hospital during 2011 to 2014 were analysed to detect cases of paracetamol overdosing in a local pharmacoepidemiological database. In 2014, an automated algorithm screened the hospital's electronic prescribing system for patients at risk of overdosing, followed by expert validation. When imminent relevant overdosing was confirmed, alerts were issued to prescribers. Relevance was defined as prescriptions that permitted repeated daily paracetamol exposure of ≥5 g. RESULTS AND DISCUSSION From 2011 to 2013, relevant overdosing occurred in 11 patients (5-8 g/day for 3 to 5 days), which corresponds to 0·4 % of all patients exposed to any paracetamol overdosing (mean n = 988 per year). In 2014, alerts were issued by experts in 23 cases with subsequent changes to prescriptions in 21 (91·3 %) thereof. Although the occurrence of any paracetamol overdosing declined only marginally in 2014 (n = 914), no relevant overdosing occurred anymore. WHAT IS NEW AND CONCLUSION Unintentional paracetamol overdosing was frequent but only a small fraction thereof was deemed relevant. This proof of concept study analysed local hospital data and developed a preventive system combining sensitive automated detection with subsequent specific expert validation. The resulting alerts achieved high compliance and prevented relevant paracetamol overdosing.

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